Targeted marketing for user conversion

ABSTRACT

A method for targeting marketing for user conversion includes receiving a list of users. Data pertaining to the users is received. A conversion likelihood score representing an estimation of how likely the user would be to converted from a trial user to a paid user is determined for each user. A similarity score representing how similar the users of the pair are to one another is determined for each possible pair of users. A graph in which each node thereof represents each user and edges between the nodes have edge weights representing the determined similarity scores is constructed. Each node is associated with a value representing its conversion likelihood score. A marketing potential score is calculated for each user using both the node-associated-values and the edge weights of the graph. A set of target users having highest marketing potential scores is constructed.

BACKGROUND

1. Technical Field

The present disclosure relates to marketing targeting and, morespecifically, to targeted marketing for user conversion.

2. Discussion of Related Art

Today it is increasingly common for users to make use of software thatis centrally hosted. Such software may be referred to as “software as aservice” (SaaS). Where the centrally hosted software makes use ofvarious shared computing resources, the offering may be referred to as“cloud computing.” A cloud computing platform is a set of resourcesoffered to entities to assist in providing their own cloud computingsolutions to their own users.

As there are a wide variety of SaaS, cloud computing, and cloudplatforms in use, it is common to offer use of these products to userson a trial basis. The trial basis may be free or low cost and may beprovided for a limited time or with a limited set of capabilities. It isthe object of the offering entity to convert these trial users to paidusers. However, it may be difficult to know which trial users to marketwhich paid options to, especially when the number of trial users isextremely large or the desired method of marketing to trial users iscomparatively expensive.

BRIEF SUMMARY

A method for targeted marketing for user conversion includes receiving alist of a plurality of users, the plurality of users including bothtrial users and paid users. Data pertaining to the plurality of users isreceived. A conversion likelihood score representing an estimation ofhow likely the user would be converted from a trial user to a paid useris determined for each user of the plurality of users. A similarityscore representing how similar the users of the pair are to one anotheris determined for each possible pair of users within the plurality ofusers. A graph in which each node thereof represents each user of theplurality of users and edges between the nodes have edge weightsrepresenting the determined similarity scores is constructed. Each nodeis associated with a value representing its conversion likelihood score.A marketing potential score is calculated for each user of the pluralityof users using both the node-associated-values and the edge weights ofthe graph. A set of target users including trial users having highestmarketing potential scores is constructed. An optimized marketingstrategy is generated from the set of target users.

The data pertaining to the plurality of users may include dynamic dataand static data. The dynamic data may include marketing response data,service consumption data, or other user data. The static data mayinclude sales data or product details.

The conversion likelihood scores for paid users may be set as a maximumvalue.

The marketing potential score may be calculated as:

$S_{i} = {{\frac{1}{\psi }{\sum\limits_{j \in \psi}{{CS}(j)}}} + {W_{ij} \times {{CS}(i)}}}$

where S_(i) is the marketing potential score for each user node i, CS(i)is the conversion likelihood score for each user node i, CS(j) is theconversion likelihood score for each user node j, W_(ij) is the edgeweight for the edge connecting each user node i with each user node j,and ψ represents a number of nodes j that are connected to each node iby an edge.

A method for selecting users for targeted marketing includes receiving alist of a plurality of users. Data pertaining to the plurality of usersis received. A conversion likelihood score representing an estimation ofhow likely the user is to consummate a purchase as a result of thetargeted marketing is determined for each user of the plurality ofusers. A similarity score representing how similar the users of the pairare to one another is determined for each possible pair of users withinthe plurality of users. A graph in which each node thereof representseach user of the plurality of users and edges between the nodes haveedge weights representing the determined similarity scores isconstructed. Each node is associated with a value representing itsconversion likelihood score. A marketing potential score is calculatedfor each user of the plurality of users using both thenode-associated-values and the edge weights of the graph. A set oftarget users including the users of the plurality of users havinghighest marketing potential scores is constructed. An optimizedmarketing strategy is generated from the set of target users.

Determining the conversion likelihood score nay include reducingdimensionality of the received data pertaining to the users, up-samplingor down-sampling the dimensionality-reduced data to remediate unbalancednature of the dimensionally-reduced data, extracting features from theup/down-sampled data to identify features having high predictive power,training a machine learning model using the extracted features to modelconversion likelihood, and applying the trained model to predict theconversion likelihood scores.

A joint optimization framework may be used to identify the set of targetusers that maximize consummation of purchases as a result of thetargeted marketing.

The data pertaining to the plurality of users may include dynamic dataand static data.

The dynamic data may include marketing response data, serviceconsumption data, or other user data.

The static data may include sales data or product details.

Determining the similarity score between two users may include miningconfiguration and activity patterns of the two users from the serviceconsumption data, the service consumption data including cloud layerconfigurations and logs, creating a customer-specific cloudconfiguration-behavior characterization for each user, and measuring thesimilarity between the two users based on the mined configuration andactivity patterns and the customer-specific cloud configuration-behaviorcharacterization.

A computer system includes a processor and a non-transitory, tangible,program storage medium, readable by the computer system, embodying aprogram of instructions executable by the processor to perform methodsteps for method for targeting marketing for user conversion. The methodincludes receiving a list of a plurality of users, the plurality ofusers including both trial users and paid users, receiving datapertaining to the plurality of users, determining, for each user of theplurality of users, a conversion likelihood score representing anestimation of how likely the user would be converted from a trial userto a paid user, determining, for each possible pair of users within theplurality of users, a similarity score representing how similar theusers of the pair are to one another, calculating, for each user of theplurality of users, a marketing potential score using both theconversion likelihood scores and the similarity scores, constructing aset of target users including trial users having highest marketingpotential scores, and generating an optimized marketing strategy fromthe set of target users.

Constructing a set of target users may include constructing a graph inwhich each node thereof represents each user of the plurality of usersand edges between the nodes have edge weights representing thedetermined similarity scores, wherein each node is associated with avalue representing its conversion likelihood score.

The marketing potential score may be calculated as:

$S_{i} = {{\frac{1}{\psi }{\sum\limits_{j \in \psi}{{CS}(j)}}} + {W_{ij} \times {{CS}(i)}}}$

where S_(i) is the marketing potential score for each user node i, CS(i)is the conversion likelihood score for each user node i, CS(j) is theconversion likelihood score for each user node j, W_(ij) is the edgeweight for the edge connecting each user node i with each user node j,and ψ represents a number of nodes j that are connected to each node iby an edge.

The data pertaining to the plurality of users may include dynamic dataand static data.

The dynamic data may include marketing response data, serviceconsumption data, or other user data.

The static data may include sales data or product details.

The conversion likelihood scores for paid users may be set as a maximumvalue.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

A more complete appreciation of the present disclosure and many of theattendant aspects thereof will be readily obtained as the same becomesbetter understood by reference to the following detailed descriptionwhen considered in connection with the accompanying drawings, wherein:

FIG. 1 is an example of a conversion likelihood/similarity score graphin accordance with exemplary embodiments of the present invention;

FIG. 2 is a flow chart illustrating an approach for performing targetedmarketing for user conversion in accordance with exemplary embodimentsof the present invention;

FIG. 3 is a schematic diagram illustrating a system for targetedmarketing for user conversion in accordance with exemplary embodimentsof the present invention; and

FIG. 4 shows an example of a computer system capable of implementing themethod and apparatus according to embodiments of the present disclosure.

DETAILED DESCRIPTION

In describing exemplary embodiments of the present disclosureillustrated in the drawings, specific terminology is employed for sakeof clarity. However, the present disclosure is not intended to belimited to the specific terminology so selected, and it is to beunderstood that each specific element includes all technical equivalentswhich operate in a similar manner.

Exemplary embodiments of the present invention seek to provide systemsand methods for automatically targeting marketing to a group of users,for example, where the group of users are trial users of a cloudplatform, cloud computing offering, SaaS, etc. However, exemplaryembodiments of the present invention are not limited to suchapplications and may be used, more generally, to target candidates forany marketing campaign.

While exemplary embodiments of the present invention may be describedherein in terms of targeting user conversion from trial users to paidusers, it is to be understood that the present invention may be appliedto targeting users for any conversion in which the user is a user of afirst product, service, or set of features, and it is desired that theuser become a user of other products, services, or sets of features,regardless of whether this use requires additional subscriptions orpayments.

Exemplary embodiments of the present invention seek to target thoseusers with a highest likelihood of conversion. However, rather thansimply calculating a conversion likelihood score for each user and thentargeting a set of highest-scored users, exemplary embodiments of thepresent invention provide a blended approach for user targeting in whicha wide variety of data is used to both calculate conversion likelihoodscores for each user and to determine an extent of similarity, e.g. asimilarity score, between the various users. A blended score isdetermined for each user. The blended score takes into account both theconversion likelihood score for that user and also the conversionlikelihood scores for other users that may be substantially similar tothe given user, where the conversion likelihood scores for the similarusers are weighted in accordance with a degree of similarity between theuser and the similar user, for example, as expressed by the similarityscore. The users with the highest blended score may then be targeted.

Data used to generate conversion scores may be noisy and/or sparse. Toreduce conversion estimation error (e.g., false positives and falsenegatives), this blended approach may be used to adjust the conversionscores in accordance to how similar a given user is to the subset ofusers with similar demographics, usage patterns, touch/response behavioretc.

In performing exemplary embodiments of the present invention, agraphical representation of the users may be generated. In such a graph,each user may be represented by a node. Edges may connect each user withother users who are correlated, for example, by similarity scoring. Theedge weight of each edge may be dependent upon the similarity score. Theconversion likelihood score may also be represented on the graph, forexample, using a node diameter that is proportional to the conversionlikelihood score, or by depicting a second set of edges that connecteach node to a common point representing the marketer. The weight ofeach of these marketer edges may be dependent upon the conversionlikelihood score. According to another approach, the conversionlikelihood score may simply be provided in association with each node,for example within the node or next to it.

FIG. 1 is an example of a conversion likelihood/similarity score graphin accordance with exemplary embodiments of the present invention. Whileit is to be understood that actual graphs created in accordance withexemplary embodiments of the present invention may include manythousands of users, the graph illustrated in FIG. 1 has been simplifiedfor the purposes of providing added clarity. In the graph, five nodesare depicted. The first node (node 11) is shown as having anext-to-largest diameter indicating a relatively high conversionlikelihood score. The second node (node 12) is shown as having a largestdiameter indicating an even higher conversion likelihood score. Thethird node (node 13) is shown as having a medium diameter indicating amoderate conversion likelihood score. The fourth node (node 14) is shownas having a smallest diameter indicating a relatively low conversionlikelihood score. The fifth node (node 15) is also shown having a mediumdiameter indicating a moderate conversion likelihood score.

An edge connecting the first node (node 11) with the second node (node12) is shown having a light edge weight indicating a relatively lowdegree of similarity between the users represented by the first andsecond nodes. An edge connecting the first node (node 11) with the thirdnode (node 13) is shown having a heavy edge weight indicating arelatively high degree of similarity between the users represented bythe first and third nodes. An edge connecting the first node (node 11)with the fourth node (node 15) is shown having a medium edge weightindicating a moderate degree of similarity between the users representedby the first and fourth nodes. The other edges are similarly depicted.It is to be understood that edges may be omitted where there is a verylow or no degree of similarity between the users represented by a pairof nodes. The edge weights may conform to one of a limited set ofpossible edge weights or they may represent any real value.

FIG. 2 is a flow chart illustrating an approach for performing targetedmarketing for user conversion in accordance with exemplary embodimentsof the present invention. First, a set of users may be received alongwith data such as dynamic data and static data (Step S201). The set ofusers may be a list of users of a cloud platform, cloud service, SaaS,etc. The set may include both trial users as well as paid users, as willbe described in greater detail below.

The dynamic data may be data pertaining to the users and services offersthat is continuously or periodically updated. Dynamic data is “dynamic”in that its values may be expected to change from time to time. Thestatic data may be data that is defined once and then rarely, if ever,updated. Static data is “static” in that it is set on the onset andassumed not to change. Together, the static data and the dynamic datamay be used either to compute conversion likelihood scores (confidencescores) or to compute similarity scores.

Examples of data used to compute confidence scores may include marketinginteraction data which indicates user engagement levels with respect tothe marketer; and static data such as opportunity data as a function oftime, sales data and product details which may help identify potentialadvocates. Examples of data used to compute similarity scores mayinclude service consumption data which helps identify users with similarbehavior and potential advocates for new service adoptions; clickstreamdata which provides cookie level activity information pertaining toproducts viewed, whitepapers read, frequency, recency, etc.; andfilmographic/demographic data, which may provide rich insight intouser's interests, skillsets, and expertise.

For each user, a confidence score may be computed (Step S202). Theconfidence score CS may be computed in accordance with any knowntechnique for estimating conversion likelihood. However, exemplaryapproaches for computing confidence scores are described in greaterdetail below with reference to the unit for computing conversionlikelihood scores which is illustrated as element 309 in FIG. 3. It isunderstood that the data used to calculate the confidence score may benoisy and/or sparse and so the confidence score itself may beinsufficient to adequately determine a marketing potential for each andevery user.

For each pair of users, a similarity score (expressed as an edge weightW) may be calculated (Step S203). This score may be computed inaccordance with any known technique for assessing similarity of people.However, exemplary approaches for computing similarity scores aredescribed in greater detail below with reference to the unit forcomputing similarity scores which is illustrated as element 313 in FIG.3

A graphical model, for example, one similar to that shown in FIG. 1, maythen be constructed for the set of users based on the confidence scoresand the similarity scores (Step S204). As described above, a node may beincluded in the graph for each user, the similarity scores between usersmay be depicted as edges between nodes, with the weight of the edgerepresenting the particular similarity score. The confidence score maybe depicted in one of a variety of ways, such as in the diameter of thenode, the color of the node, a value associated with the node, etc.

Then, an optimal set of users may be identified such that directingmarketing efforts to this optimal set of users maximizes conversions(Step S205). This may be done, for example, by calculating a marketingpotential score (S) for each node of the graph. The following equation(eq. 1) may be used as an example of a manner in which marketingpotential scores may be calculated in accordance with exemplaryembodiments of the present invention:

$\begin{matrix}{S_{i} = {{\frac{1}{\psi }{\sum\limits_{j \in \psi}{{CS}(j)}}} + {W_{ij} \times {{CS}(i)}}}} & \left\lbrack {{Eq}.\mspace{11mu} 1} \right\rbrack\end{matrix}$

For each node i, all nodes having at least some measurable degree ofconnection to that node (quantity of this set denoted as ψ) are foundand used to calculate S_(i), which is the marketing potential score forthat node, based on CS(i), which is the confidence score for node i,CS(j), which is the confidence score for node j (where j represents eachconnected node from 1 to ψ), and W_(ij), which is the edge weightindicative of the similarity score between nodes i and j.

After the marketing potential score S has been calculated for each node,the nodes may be sorted by S and a set of N top nodes may be determined.An optimized marketing strategy may then be generated for the set of Ntop nodes (Step S206).

As discussed above, the set of users may include both trial users andpaid users, even though there might be no interest in actually targetingusers who are already paid users, exemplary embodiments of the presentinvention may still include these users in the set of users, calculatetheir respective conversion likelihood score, as if they were notalready paid users, and may calculate their similarity scores. In thisway, more information may be gleamed about the trail users, especiallythose who are strongly connected to the paid users.

According to some exemplary embodiments of the present invention, theconfidence score for paid users may be set to a maximum value, which maybe 1.0 on a scale from 0.0 to 1.0. Similarly, the marketing potentialscore S may be set to maximum (e.g. 1.0) for these nodes.

However, regardless of whether conversion likelihood scores arecalculated or assigned as maximum for the paid users, nodes identifiedas paid users may be omitted from the list of top N nodes, and thus, thelist of top N nodes may actually be those nodes with the top marketingpotential score who are not paid users.

FIG. 3 is a schematic diagram illustrating a system for targetedmarketing for user conversion in accordance with exemplary embodimentsof the present invention. As discussed above, various dynamic data 300and static data 304 may be used. The dynamic data 300 may includedatabase sources such as marketing response data 301, serviceconsumption data 302, and user data 303. The static data 304 may includedatabase sources such as sales data 305 and product details 306.

This data may be conditioned for use by an entity resolution unit 307which may resolve duplicate and inconsistent data and may merge thedata. The conditioned and merged data may be stored in a merged datadatabase 308. From there, the conditioned and merged data may be used bya conversion likelihood scoring unit 309 to compute the conversionlikelihood scores for each user, as described above; and may be used bya similarity scoring unit 313 to compute the similarity scores betweenthe various pairs of users.

A joint optimization framework 314 may then be used to identify theoptimal set of users to be contacted to maximize the number ofconversions. The joint optimization framework 314 may provide, asoutput, an optimized marketing strategy.

The conversion likelihood scoring unit 309 may include a dimensionalityreduction unit 310 which may perform dimensionality reduction on thedata to learn hidden behavioral patterns. The conversion likelihoodscoring unit 309 may also include a sampling unit 311 for performingupsampling and downsampling techniques to handle unbalanced data. Theconversion likelihood scoring unit 309 may also include a featureextraction unit 312 which may perform feature extraction to identifythose features with high predictive power.

The conversion likelihood scoring unit 309 may examine historicalinteractions with users and may use these historical interactions astraining data. The features for predicting conversion likelihood may bemodeled from this training data. For example, ensemble modeling may beperformed using a classification tree, logistic registration, neuralnetwork, Bayesian network, CHAID, etc. Multiple models may be used andthen predicted outcome (conversion from trial to paid) may be based onconfidence-weighted voting of the multiple models used.

The similarity scoring unit 313 may mine user data from within the couldplatform to determine configuration and/or activity patterns. However,as actual data from cloud platforms may be low level and diverse, directpatterning and similarity mining may be facilitated by: (1) mappingconfiguration and log elements to a unified taxonomy with well-definedterms and attributes; (2) employing pattern mining, frequency logmining, etc.; and (3) for each user and layer, measuring low levelstatistical similarity and activity/behavior similarity.

As individual layer patterns and similarities may not be discriminatoryenough, for example, many users may use similar software features,exemplary embodiments of the present invention may create auser-specific cloud configuration-behavior characterization(“fingerprint”) which may be used to both uniquely identify a user andallow for generating a meaningful user similarity metric. This may beperformed by: (1) computing customer cloud fingerprints as a vector of aunion of patterns across layers plus a minimal distinction feature set.The minimal distinction feature set may be a subset of features from theuser cloud fingerprint which uniquely, or nearly uniquely, andminimally, characterize the user. User cloud fingerprintsimilarity/distance may be calculated as a vector basedsimilarity/distance, for example, a cosine similarity/Euclidiandistance, between two fingerprints where at each dimension of thevector, the lower level similarity/distance is used to measuredimension-level similarity/distance.

FIG. 4 shows an example of a computer system which may implement amethod and system of the present disclosure. The system and method ofthe present disclosure may be implemented in the form of a softwareapplication running on a computer system, for example, a mainframe,personal computer (PC), handheld computer, server, etc. The softwareapplication may be stored on a recording media locally accessible by thecomputer system and accessible via a hard wired or wireless connectionto a network, for example, a local area network, or the Internet.

The computer system referred to generally as system 1000 may include,for example, a central processing unit (CPU) 1001, random access memory(RAM) 1004, a printer interface 1010, a display unit 1011, a local areanetwork (LAN) data transmission controller 1005, a LAN interface 1006, anetwork controller 1003, an internal bus 1002, and one or more inputdevices 1009, for example, a keyboard, mouse etc. As shown, the system1000 may be connected to a data storage device, for example, a harddisk, 1008 via a link 1007.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowcharts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

Exemplary embodiments described herein are illustrative, and manyvariations can be introduced without departing from the spirit of thedisclosure or from the scope of the appended claims. For example,elements and/or features of different exemplary embodiments may becombined with each other and/or substituted for each other within thescope of this disclosure and appended claims.

What is claimed is:
 1. A method for targeted marketing for userconversion, comprising: receiving a list of a plurality of users, theplurality of users including both trial users and paid users; receivingdata pertaining to the plurality of users; determining, for each user ofthe plurality of users, a conversion likelihood score representing anestimation of how likely the user would be converted from a trial userto a paid user; determining, for each possible pair of users within theplurality of users, a similarity score representing how similar theusers of the pair are to one another; constructing a graph in which eachnode thereof represents each user of the plurality of users and edgesbetween the nodes have edge weights representing the determinedsimilarity scores, wherein each node is associated with a valuerepresenting its conversion likelihood score; calculating, for each userof the plurality of users, a marketing potential score using both thenode-associated-values and the edge weights of the graph; constructing aset of target users including trial users having highest marketingpotential scores; and generating an optimized marketing strategy fromthe set of target users.
 2. The method of claim 1, wherein the datapertaining to the plurality of users includes dynamic data and staticdata.
 3. The method of claim 2, wherein the dynamic data includesmarketing response data, service consumption data, or other user data.4. The method of claim 2, wherein the static data includes sales data orproduct details.
 5. The method of claim 1, wherein the conversionlikelihood scores for paid users is set as a maximum value.
 6. Themethod of claim 1, wherein the marketing potential score is calculatedas:$S_{i} = {{\frac{1}{\psi }{\sum\limits_{j \in \psi}{{CS}(j)}}} + {W_{ij} \times {{CS}(i)}}}$where S_(i) is the marketing potential score for each user node i, CS(i)is the conversion likelihood score for each user node i, CS(j) is theconversion likelihood score for each user node j, W_(ij) is the edgeweight for the edge connecting each user node i with each user node j,and ψ represents a number of nodes j that are connected to each node iby an edge.
 7. A method for selecting users for targeted marketing,comprising: receiving a list of a plurality of users; receiving datapertaining to the plurality of users; determining, for each user of theplurality of users, a conversion likelihood score representing anestimation of how likely the user is to consummate a purchase as aresult of the targeted marketing; determining, for each possible pair ofusers within the plurality of users, a similarity score representing howsimilar the users of the pair are to one another; constructing a graphin which each node thereof represents each user of the plurality ofusers and edges between the nodes have edge weights representing thedetermined similarity scores, wherein each node is associated with avalue representing its conversion likelihood score; calculating, foreach user of the plurality of users, a marketing potential score usingboth the node-associated-values and the edge weights of the graph;constructing a set of target users including the users of the pluralityof users having highest marketing potential scores; and generating anoptimized marketing strategy from the set of target users.
 8. The methodof claim 7, wherein determining the conversion likelihood score,comprises: reducing dimensionality of the received data pertaining tothe users; up-sampling or down-sampling the dimensionality-reduced datato remediate unbalanced nature of the dimensionally-reduced data;extracting features from the up/down-sampled data to identify featureshaving high predictive power; training a machine learning model usingthe extracted features to model conversion likelihood; and applying thetrained model to predict the conversion likelihood scores.
 9. The methodof claim 7, wherein a joint optimization framework is used to identifythe set of target users that maximize consummation of purchases as aresult of the targeted marketing.
 10. The method of claim 7, wherein thedata pertaining to the plurality of users includes dynamic data andstatic data.
 11. The method of claim 10, wherein the dynamic dataincludes marketing response data, service consumption data, or otheruser data.
 12. The method of claim 10, wherein the static data includessales data or product details.
 13. The method of claim 11, whereindetermining the similarity score between two users, comprises: miningconfiguration and activity patterns of the two users from the serviceconsumption data, the service consumption data including cloud layerconfigurations and logs; creating a customer-specific cloudconfiguration-behavior characterization for each user; and measuring thesimilarity between the two users based on the mined configuration andactivity patterns and the customer-specific cloud configuration-behaviorcharacterization.
 14. A computer system comprising: a processor; and anon-transitory, tangible, program storage medium, readable by thecomputer system, embodying a program of instructions executable by theprocessor to perform method steps for method for targeting marketing foruser conversion, the method comprising: receiving a list of a pluralityof users, the plurality of users including both trial users and paidusers; receiving data pertaining to the plurality of users; determining,for each user of the plurality of users, a conversion likelihood scorerepresenting an estimation of how likely the user would be convertedfrom a trial user to a paid user; determining, for each possible pair ofusers within the plurality of users, a similarity score representing howsimilar the users of the pair are to one another; calculating, for eachuser of the plurality of users, a marketing potential score using boththe conversion likelihood scores and the similarity scores; constructinga set of target users including trial users having highest marketingpotential scores; and generating an optimized marketing strategy fromthe set of target users.
 15. The computer system of claim 14, whereinconstructing a set of target users includes constructing a graph inwhich each node thereof represents each user of the plurality of usersand edges between the nodes have edge weights representing thedetermined similarity scores, wherein each node is associated with avalue representing its conversion likelihood score.
 16. The computersystem of claim 15, wherein the marketing potential score is calculatedas:$S_{i} = {{\frac{1}{\psi }{\sum\limits_{j \in \psi}{{CS}(j)}}} + {W_{ij} \times {{CS}(i)}}}$where S_(i) is the marketing potential score for each user node i, CS(i)is the conversion likelihood score for each user node i, CS(j) is theconversion likelihood score for each user node j, W_(ij) is the edgeweight for the edge connecting each user node i with each user node j,and ψ represents a number of nodes j that are connected to each node iby an edge.
 17. The computer system of claim 14, wherein the datapertaining to the plurality of users includes dynamic data and staticdata.
 18. The computer system of claim 17, wherein the dynamic dataincludes marketing response data, service consumption data, or otheruser data.
 19. The computer system of claim 17, wherein the static dataincludes sales data or product details.
 20. The computer system of claim14, wherein the conversion likelihood scores for paid users is set as amaximum value.